Recursive handwriting recognition using epistemic utility theory

In most handwriting recognition systems, the processing steps of segmentation, recognition, decision making and post-processing are serially taken. These systems usually use the resources exhaustively in each stage of the serial engine. Every stage is tuned to maximize global performance. However, such a paradigm is prone to miss the global optimal point because the individual stages often do not have all the information available. A more interactive model is desirable. In this paper we describe such a model extending the concepts of satisfying decision mechanisms. Our experiments show proof of validity of the proposed model